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dc.contributor.authorBracher, Johannes
dc.contributor.authorWolffram, Daniel
dc.contributor.authorDeuschel, Jannik
dc.contributor.authorGörgen, Konstantin
dc.contributor.authorKetterer, Jakob L.
dc.contributor.authorUllrich, Alexander
dc.contributor.authorAbbott, Sam
dc.contributor.authorBarbarossa, Maria Vittoria
dc.contributor.authorBertsimas, Dimitris
dc.contributor.authorBhatia, Sangeeta
dc.contributor.authorBodych, Marcin
dc.contributor.authorBosse, Nikos I.
dc.contributor.authorBurgard, Jan Pablo
dc.contributor.authorCastro, Lauren
dc.contributor.authorFairchild, Geoffrey
dc.contributor.authorFuhrmann, Jan
dc.contributor.authorFunk, Sebastian
dc.contributor.authorGogolewski, Krzysztof
dc.contributor.authorGu, Quanquan
dc.contributor.authorHeyder, Stefan
dc.contributor.authorHotz, Thomas
dc.contributor.authorKheifetz, Yuri
dc.contributor.authorKirsten, Holger
dc.contributor.authorKrueger, Tyll
dc.contributor.authorKrymova, Ekaterina
dc.contributor.authorLi, Michael Lingzhi
dc.contributor.authorMeinke, Jan H.
dc.contributor.authorMichaud, Isaac
dc.contributor.authorNiedzielewski, Karol
dc.contributor.authorOżański, Tomasz
dc.contributor.authorRakowski, Franciszek
dc.contributor.authorScholz, Markus
dc.contributor.authorSoni, Saksham
dc.contributor.authorSrivastava, Ajitesh
dc.contributor.authorZielinski, Jakub
dc.contributor.authorZou, Difan
dc.contributor.authorGneiting, Tilmann
dc.contributor.authorSchienle, Melanie
dc.date.accessioned2022-04-08T15:46:12Z
dc.date.available2022-04-08T15:46:12Z
dc.date.issued2021
dc.identifier.citationBracher, J., Wolffram, D., Deuschel, J. et al. A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave. Nat Commun 12, 5173 (2021). https://doi.org/10.1038/s41467-021-25207-0en
dc.identifier.issn2041-1723
dc.identifier.other10.1038/s41467-021-25207-0
dc.identifier.urihttps://depot.ceon.pl/handle/123456789/21244
dc.descriptionThe online version contains supplementary materialavailable athttps://doi.org/10.1038/s41467-021-25207-0.en
dc.description.abstractDisease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.en
dc.description.sponsorshipOpen Access funding enabled and organized by Projekt DEAL
dc.language.isoen
dc.publisherSpringer Natureen
dc.rightsCreative Commons Uznanie autorstwa 4.0*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcode*
dc.subjectCOVID-19en
dc.subjectdisease modellingen
dc.titleA pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second waveen
dc.typearticleen
dc.contributor.organizationKarlsruhe Institute of Technology (KIT), Karlsruhe, Germanyen
dc.contributor.organizationComputational Statistics Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germanyen
dc.contributor.organizationRobert Koch Institute (RKI), Berlin, Germanyen
dc.contributor.organizationLondon School of Hygiene and Tropical Medicine, London, UKen
dc.contributor.organizationFrankfurt Institute for Advanced Studies, Frankfurt, Germanyen
dc.contributor.organizationSloan School of Management, Massachusetts Institute of Technology, Cambridge, USAen
dc.contributor.organizationAbdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), Imperial College London, UKen
dc.contributor.organizationWroclaw University of Science and Technology, Wroclaw, Polanden
dc.contributor.organizationEconomic and Social Statistics Department, University of Trier, Germanyen
dc.contributor.organizationInformation Systems and Modeling, Los Alamos National Laboratory, Los Alamos, USAen
dc.contributor.organizationInstitute of Informatics, University of Warsaw, Polanden
dc.contributor.organizationDepartment of Computer Science, University of California, Los Angeles, USAen
dc.contributor.organizationInstitute of Mathematics, Technische Universität Ilmenau, Germanyen
dc.contributor.organizationInstitute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Germanyen
dc.contributor.organizationSwiss Data Science Center, ETH Zurich and EPFL, Lausanne, Switzerlanden
dc.contributor.organizationOperations Research Center, Massachusetts Institute of Technology, Cambridge, USAen
dc.contributor.organizationJülich Supercomputing Centre, Forschungszentrum Jülich, Germanyen
dc.contributor.organizationStatistical Sciences Group, Los Alamos National Laboratory, Los Alamos, USAen
dc.contributor.organizationInterdisciplinary Centre for Mathematical and Computational Modelling, University of Warsawen
dc.contributor.organizationMing Hsieh Department of Computer and Electrical Engineering, University of Southern California, Los Angeles, USAen


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Creative Commons Uznanie autorstwa 4.0
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